Qiongkai Xu

Dr

Calculated based on number of publications stored in Pure and citations from Scopus
20122023

Research activity per year

Personal profile

Biography

Dr. Qiongkai Xu is a Lecturer in Computing at Macquarie University, having earned his Ph.D. from the Australian National University and previously served as a research fellow at the University of Melbourne. His research primarily focuses on Natural Language Processing, Privacy & Security, Machine Learning and Data Mining. Recently, his attention has been directed towards auditing machine learning models, specifically in two areas: 1) identifying and addressing privacy and security issues in ML/NLP models and their applications and 2) developing comprehensive evaluation theory and methods for ML/NLP models from various perspectives.

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Research interests

My research spans a range of disciplines, with a focus on areas such as Natural Language Processing (NLP), Machine Learning (ML), Security & Privacy, Artificial Intelligence (AI), and Data Mining (DM). I have an active publication record in these fields, such as ACL, EMNLP, NeurIPS, ICLR, AAAI, WWW, WSDM, CIKM. Recently, my focus has shifted to more specific topics, including:

  • Text Watermarking and Fingerprinting (NLP/ML+Security); 
  • Backdoor and Adversarial Attack (NLP/ML+Security+DM); 
  • Data Leakage in Language Model (NLP/ML+Security); 
  • Performance Evaluation (NLP/ML). 

Education/Academic qualification

Natural Language Processing, PhD, Privacy Protection in Conversations, Australian National University

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Collaborations and top research areas from the last five years

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  • Boot and switch: alternating distillation for zero-shot dense retrieval

    Jiang, F., Xu, Q., Drummond, T. & Cohn, T., 2023, Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, PA: Association for Computational Linguistics (ACL), p. 912-931 20 p.

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    Open Access
  • Fingerprint attack: client de-anonymization in federated learning

    Xu, Q., Cohn, T. & Ohrimenko, O., 2023, ECAI 2023: 26th European Conference on Artificial Intelligence September 30–October 4, 2023, Kraków, Poland including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023) proceedings. Gal, K., Nowé, A., Nalepa, G. J., Fairstein, R. & Rădulescu, R. (eds.). Amsterdam, Netherlands: IOS Press, p. 2792-2801 10 p. (Frontiers in Artificial Intelligence and Applications; vol. 372).

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    Open Access
    File
    9 Downloads (Pure)
  • Humanly certifying superhuman classifiers

    Xu, Q., Walder, C. & Xu, C., 2 Feb 2023, (Submitted) The Eleventh International Conference on Learning Representations: ICLR 2023. Appleton, WI

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

  • Mitigating backdoor poisoning attacks through the lens of spurious correlation

    He, X., Xu, Q., Wang, J., Rubinstein, B. & Cohn, T., 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, p. 953-967 15 p.

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    Open Access
  • Rethinking round-trip translation for machine translation evaluation

    Zhuo, T. Y., Xu, Q., He, X. & Cohn, T., 2023, Findings of the Association for Computational Linguistics: ACL 2023. Rogers, A., Boyd-Graber, J. & Okazaki, N. (eds.). Kerrville, TX: Association for Computational Linguistics, p. 319-337 19 p.

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    Open Access